Method and apparatus for characterising multiphase fluid mixtures

ABSTRACT

A method for determining at least one characteristic of a multiphase fluid including the steps of applying alternating energy of a predetermined amplitude to a portion of a multiphase fluid and measuring the electrical impedance spectrum across the portion of multiphase fluid whereby a characteristic of the multiphase fluid can be determined from the measured electrical impedance spectra.

TECHNICAL FIELD

This invention relates to a method and apparatus for characterisingmultiphase fluid mixtures (e.g. slurries, emulsions, suspension ofbubbles and fine solids in liquid, and bubble froth phase) based on theanalysis of electrical impedance spectrum using predictive mathematicalalgorithms, such as artificial neuron network.

BACKGROUND OF THE INVENTION

In many industrial processes involving multiphase fluid mixtures wherethe components and mixtures may be stationary, moving in bathes orflowing continuously, there are needs for accurate and inexpensive phaseconcentration monitoring methods and means. It is also often desirablethat these methods and means have the capability of working on-line withthe processes.

A number of methods have been used in the past to monitor the phaseconcentration of multiphase fluid mixtures. Generally these methods seekto find a specific property which is significantly different for thephases. The value of this property for the mixture will then depend onthe phase concentration. By measuring this property one would be able tofind the phase concentration. Examples of the specific property areelectrical properties (i.e. conductivity or capacitance), density,viscosity, absorption of light or absorption of radioactive radiation.

Precise and safe measurement of conductivity or capacitance requiresrelatively simple instrumentation. Thus, methods based on conductivityor capacitance have been widely used in practice for measuring phaseconcentrations not only in solids-liquid systems but also in gas-liquid,liquid-liquid and three-phase systems.

Examples of conductivity or capacitance based devices are disclosed inU.S. Pat. No. 4,266,425 to Allport et al., U.S. Pat. No. 3,523,245 toLove et al.

The prior art systems described above, however, have a few of majordrawbacks. Electrical conductivity based methods are very sensitive tothe variations in the electrical conductivity of the liquid phase of themultiphase fluid mixture. For example, the electrical conductivity of anaqueous slurry may increase by more than 50 times with the addition of2.5% by weight of salt (NaCl) to the aqueous phase. When theconductivity of the liquid phase changes substantially with time, theconductivities of both the slurry mixture and the liquid phase arerequired in order to calculate the solids concentration. But the on-linemeasurement of the conductivity of the liquid phase in a slurry mixtureis generally difficult due to the requirement of phase separation.Electrical conductivity based methods are also generally difficult toapply to multiphase fluid mixtures having very low electricalconductivity. The capacitance based methods can be applied only tomultiphase fluid mixture where the continuous phase is nonconductive. Inthe case with aqueous slurries, the high electrical conductivity of theaqueous phase interferes with the dielectric measurement.

In determining the water content of oil/water emulsion mixtures, priorart systems have a significant limitation because of the fact that theelectrical properties of water-continuous and oil-continuous emulsionsare quite different even if the water content is identical. Prior artsystems have also failed to provide methods or means for determiningphase composition in fluid mixtures with more than two phases. This isbecause that different sets of phase compositions may result in similarconductivity or capacitance measurements. Most of prior art systemsfailed to give accurate measurements when the concentration of thedisperse phase in a two phase fluid mixture is low.

The limitation of conductivity or capacitance based methods isattributed to the limited information obtained at a single frequency ofexcitation alternating current (AC) signal. One known value of mixtureconductivity or capacitance is insufficient to determine the phasecomposition when both the phase composition and the electricalproperties of one of the phases in the mixture are unknown.

In certain industrial processes, such as dense medium separation of coaland mineral ores and grinding circuits in mineral processing industry,it is desirable to monitor the average particle size of suspended fineparticles in an aqueous slurry under the condition of high solidsconcentration. At present there are no simple commercially availableon-line particle size monitors capable of this measurement. Theconventional method of measuring particle size distribution is to removesamples from the streams of interest and to perform screen analyses onthese samples. However, screen analysis can provide a reasonablyaccurate determination of particle size distribution above about 45microns. There are three commercially available on-line particle sizeanalysers based on ultrasonic attenuation, a scanning laser microscopeand a reciprocating caliper. However, these analysers are not suitablefor use in slurry mixtures where the average particle size is below 45micron or the solids concentration is high or the fluid medium is nottransparent.

Froth flotation is widely used for concentrating minerals, or othervaluable constituents, from their ores or other raw materials. Mineralsare separated from gangue particles by taking advantage of theirdifferences in hydrophobicity. These differences can occur naturally, orcan be controlled by the addition of a collector reagent. Frothflotation generally involves the use of air injection through a slurrythat contains water, minerals and gangue particles within a vessel.Dispersed air bubbles attract the hydrophobic valuable minerals andcarry them upward to the top of the flotation cell, whereupon they forma froth bed or froth layer which contains and supports pulverisedmineral. The froth is then scraped or permitted to flow over the lip ofthe cell to effect the separation. The thus concentrated mineral bearingfroth is collected and further processed to improve the concentration ofdesired minerals. The pulp may be further processed to recover othervaluable minerals.

On-line measurement of process parameters is a prerequisite for frothflotation process control. Whereas some process parameters can bemonitored on-line with cost effective and reliable measuring devices,the effective on-line monitoring and optimal control of froth flotationprocesses are still far from being achieved because of the stronginertia of the flotation process, a still inadequate knowledge ofsuitable variables for the on-line monitoring of the process efficiencyand the lack of appropriate on-line measurement instrumentation.

The froth phase in a froth flotation process has a number ofcharacteristics, including bubble size, stability, mobility, solidscontent and water content. The effects of operating conditions such asreagent type, reagent dosage, water chemistry, pulp level, feed flowrateand aeration rate are reflected in the froth characteristics.

The characteristics of froth layer are related to flotation grade andrecovery. In view of the difficulty in the direct measurement of thefroth characteristics, it is desirable to use other froth propertiesthat can be easily on-line measured as monitoring tools and are closelyrelated to flotation grade and recovery.

SUMMARY OF THE INVENTION

The present invention provides an alternative method and apparatus forcharacterising multiphase fluid mixtures preferably where the componentsand mixtures may be stationary, moving in bathes or flowing continuouslyin a conduit, and more particularly a method and apparatus fordetermining the proportion of each phase constituting a multiphase fluidmixture, the type of oil/water emulsion mixtures, the particle size offine particles in liquid-solids slurries and the characteristics ofbubble froth phase.

In the description hereafter, “electrical impedance spectrum” refers tothe complex plane plot of imaginary verses real impedance values for aplurality of different frequencies of energy or in the plotting ofquantities derived from the real and imaginary impedance values.

According to one aspect of the present invention there is provided amethod for determining at least one characteristic of a multiphase fluidincluding the steps of applying alternating energy of a predeterminedamplitude to a portion of a multiphase fluid and measuring theelectrical impedance spectrum across the portion of multiphase fluidwhereby a characteristic of the multiphase fluid can be determined fromthe measured electrical impedance spectra.

It is preferred that the above method is repeated for a plurality ofdifferent amplitudes of alternating energy.

It is preferred that the alternating energy includes alternating voltageand alternating current.

Preferably the electrical impedance spectrum is measured across theportion of multiphase fluid for an AC voltage of constant amplitude orAC current of constant amplitude.

Preferably the alternating energy is applied across electrodes in theportion of multiphase fluid.

The term “electrodes” should be interpreted in its broadest sense toinclude any terminal, wires, or similar points across which current orvoltage can be applied to measure the electrical impedance spectrum.

It is preferred that the electrodes are set a predetermined distanceapart and electrical impedance measurements ar made at the predetermineddistance of separation between electrodes.

According to one preferred embodiment of the invention there is providedan apparatus for characterising multiphase fluid mixtures, the apparatusincluding:

an electrode pair comprising at least one conductive path and definingtherebetween and thereabout a sample zone within the multiphase fluidmixture, a measuring means for measuring characteristics of theelectrical field formed between the electrode pair, and

computing means for collecting information from the measuring means andconverting it to a desired form of output.

According to one embodiment of the invention the apparatus includes twoelectrodes.

Preferably the EIS is measured across the electrodes for a constantamplitude of potential difference (voltage).

According to another embodiment the apparatus includes three electrodes.

Preferably the current between adjacent electrodes is set at apredetermined amplitude.

It is preferred that the voltage is measured across the threeelectrodes.

According to a further embodiment of the present invention the apparatusincludes four electrodes.

Preferably the four electrodes comprise two pairs of electrodes eachadapted to provide a constant magnitude of current between the pairs ofelectrodes.

Preferably the measuring means is adapted to measure the change involtage between the pairs of electrodes.

It is preferred that the apparatus includes a configuration ofelectrodes in which either a current of constant amplitude is appliedacross the electrodes, a voltage of constant amplitude is applied acrossthe electrodes or a combination of current of constant amplitude andvoltage of constant amplitude is applied across respective adjacentelectrodes.

It is preferred that a current of constant amplitude is provided bygrouping a pair of electrodes.

According to another embodiment it is preferred that electricalimpedance spectra are measured for different values of constant voltageor alternatively different values of constant current.

According to one aspect of the present invention there is provided amethod for characterising a multiphase fluid mixture, the methodincluding the steps of

applying an alternating current or voltage to the electrodes located inthe multiphase fluid mixture,

measuring the electrical impedance spectrum across the electrodes at oneor a few selected amplitudes of excitation signal;

transforming the measured electrical impedance spectrum or spectra intoa few indicator quantities using feature extraction algorithms; and

determining, from the indicator quantities of the measured electricalimpedance spectrum, at least one characteristic of at least one phaseconstituent in the mixture using a predictive mathematical model.

According to one embodiment, the invention involves measuring the realand imaginary parts of the impedance over a frequency range of 0.1 Hz to1 MHz. Real and imaginary impedance values preferably include real andimaginary components of mathematically related parameters such asimpedance, admittance, modulus and dielectric permittivity, etc. Theimpedance sensing means may be configured in two or three or fourelectrodes.

It is preferred that the method includes the step of measuringtemperature and pH value of the multiphase fluid mixture at eachmeasurement frequency or measured impedance spectra.

The multiphase fluid mixtures preferably include matter such as gas,solid, liquid or different combinations of the above.

It is preferred that the method includes the step of transforming themeasured electrical impedance spectrum into a few indicator quantities.The step of transforming further includes obtaining data in the form ofaverage number of good readings, calculating a smoothed electricalimpedance spectrum using 2D data smoothing algorithms, such as locallyweighted regression, and scaling indicator quantities and temperatureand pH readings into suitable value ranges. The indicator quantities ofthe electrical impedance spectrum may further include the real andimaginary impedance values at a number of selected frequencies, firstand second derivatives of the spectrum at a number of selectedfrequencies, average values of imaginary impedance component over aselective range of real impedance, the parameters of a mathematicalmodel for the representation of the impedance spectrum, and the latentvariables, summarising the information contained in the originalimpedance spectrum, calculated from multivariate statistical methods,such as principal component analysis (PCA) and partial least-squares(PLS).

The method preferably includes calculating indicator quantities byfitting the electrical impedance spectrum to a mathematical model of theimpedance spectrum. The mathematical model may further include anelectrical equivalent circuit model and empirical regression equations.

Preferably the method includes the step of determining, from theindicator quantities combined with temperature and pH, at least onecharacteristic of at least one phase constituent in the mixture using apredictive mathematical model. The characteristics further include theproportion of each phase constituting a multiphase fluid mixture, thetype of oil/water emulsion mixtures, the particle size of fine particlesin liquid-solids slurries and the characteristics of bubble froth phase.The predictive mathematical model further include a trained artificialneural network and a multivariate regression model.

It is preferred that the method includes training and validating anartificial neural network with a number of indicator quantities withknown characteristics of multiphase fluid mixtures. The method furtherincludes calculating parameters in a predictive mathematical model usinga number of indicator quantities with known characteristics ofmultiphase fluid mixtures.

It is preferred that the method includes the step of analysing theimpedance spectrum using pattern matching algorithms to determinewhether characteristics of a bubble froth phase loaded with particlesare favorable or not in terms of the grade and yield of the flotationconcentrate.

The method preferably includes the step of analysing the impedancespectrum using pattern matching algorithms to determine whether anoil/water emulsion is oil continuous or water continuous type.

According to a further aspect of the present invention there is provideda method of analysing extraneous matter in a fluid including the stepsof receiving impedance data, being data including real and imaginaryimpedance values measured across electrodes located in a fluid,recording the impedance spectrum at a plurality of time intervals,calculating indicator quantities of the impedance spectrum for thereceived impedance spectrum data, comparing indicator quantities of theimpedance spectrum with reference indicator quantities and determiningat least one characteristic of at least one phase constituent in themixture from the comparing steps.

The method may include the step of determining characteristics ofmultiphase fluid mixtures in the forms of numerical value or qualitativeindex.

It should be noted that reference to electrical impedance spectrumrefers to EIS (Electrical Impedance Spectrum).

It is to be understood that, if any prior art publication is referred toherein, such reference does not constitute an admission that thepublication forms a part of the common general knowledge in the art, inAustralia or in any other country.

The words “comprising, having, including” should be interpreted in aninclusive sense, meaning that additional features may also be added.

BRIEF DESCRIPTION OF DRAWINGS

Preferred embodiments of the present invention will now be described byway of example only with reference to the accompanying drawings inwhich:

FIG. 1 is a block diagram of the measurement apparatus forcharacterising multiphase fluid mixtures according to a first embodimentof the present invention;

FIGS. 2A, 2B, 2C and 2D are schematic diagrams illustrating possibledesigns of electrode pairs useful for the measurement of electricalimpedance spectrum;

FIGS. 3A and 3B show graphical representations of electrical impedancespectra of liquid-solids slurries with different compositions;

FIGS. 4A and 4B show graphical representations of electrical impedancespectra of sugar syrup with different sugar crystal contents;

FIGS. 5A and 5B show graphical representations of electrical impedancespectra of water-oil emulsions;

FIG. 6 shows graphical representations of electrical impedance spectraof liquid-solids slurries with different particle sizes;

FIG. 7 shows graphical representations of electrical impedance spectraof froth (or foaming) phase with different characteristics;

FIG. 8 shows a schematic diagram of a method for characterisingmultiphase fluid mixtures according to one embodiment of the presentinvention; and

FIG. 9 shows a PCA neural network to project the data from D to Mdimensions.

It should be understood that the embodiments of the invention describedhereinafter with reference to the drawings refer to specific electrodeconfigurations where the electrode type, number of electrodes anddistance between electrodes remains fixed. The invention also coversother embodiments where different numbers and distances betweenelectrodes are provided as well as different types of electrodes. Inthese other embodiments values of electrical impedance would bedifferent to those exemplified in the preferred embodiments.

DETAILED DESCRIPTION

According to one embodiment of the present invention the electricalimpedance spectrum of a multiphase fluid mixture was measured over awide range of frequencies to identify characteristic parameters ofinterest in a multiphase fluid mixture. In addition the inventors notedthe dependence of electrical properties of constituents in multiphasefluid mixtures upon excitation by an AC signal varies. It was thereforeconsidered that the electrical impedance spectrum of a multiphase fluidmixture measured over a wide range of frequency may contain sufficientinformation for deducing characteristic parameters of interest.

The inventors also realised that electrical and dielectric properties ofsolids-liquid suspensions depend on not only the phase composition butalso the particle size of solids. When an AC current is passing througha suspension, the surface charge and the associated electrical doublelayer of particles tend to cause a phase shift of the AC current in acertain range of frequency due to charge relaxation processes on thesurface. For a given volume fraction of suspended particles, the smallerthe particle size, the higher the amount of the surface charge. Sincethe phase shift is proportional to the amount of the surface charge,small particles will cause a higher phase shift than large particles. Itis, therefore, possible to calculate the particle size from measuredreal and imaginary parts of electrical impedance over a wide range offrequency.

Furthermore the inventors discovered that the electrical and dielectricproperties of components in a froth phase are different from each other.

From the viewpoint of electrical behaviour of the froth phase, theinter-bubble lamellae containing water and solids can be regarded as acomplex network of electrical conductance, inductance and capacitance.The structure of this network would be sensitive to changes in the frothstructure and characteristics. Therefore, the measurement of theelectrical impedance of the froth phase over a wide range of frequencieswould probe into the froth structure and/or characteristics.

As shown in FIG. 1 an apparatus for characterising multiphase fluidmixtures consists of a pair of fluid measurement electrodes 11 a and 11b immersed in multiphase fluid mixtures 12, a temperature sensor 13, anEIS and temperature measurement unit 14, a computing unit 15 and anoutput unit 16.

Referring to FIGS. 2A-2D, the measurement electrodes can be mounted onthe inner surface of a conduit or vessel wall in the forms of tappedrods 20 a and 20 b or rings 23 a and 23 b. Alternatively the electrodescan be mounted on a non-conductive rod 24 in the form of dots 25 a and25 b, or on a non-conductive spacer 26 in the form of plates 27 a and 27b with any suitable shapes. Instead of the plate type of electrodes 27 aand 27 b, one electrode may be a rod electrode surrounded coaxially byanother cylindrical electrode. In the cases with a conductive conduit orvessel wall, the electrodes 20 a and 20 b have to be insulated with theconductive wall 21 and a non-conductive layer 22 has to be applied tocover the inner surface of the conductive wall 21. The material fornon-conductive layer 22 includes certain ceramics, casting basil,plastics and other suitable materials.

An electrical impedance spectrum and temperature measurement unit 14 isconnected to each of electrodes as well as to the computing unit 15. Themeasurement unit 14 sends and receives signals to or from the computingunit 15 through electrical, optical, electromagnetic wireless or othertype signals. The output unit 16 preferably is a visual displayer, e.g.LCD, for displaying the results provided by the computing unit 15.

The measurement unit 14 preferably includes a signal generation modulefor generating AC signals at specified amplitude and frequencies, ameasuring module for measuring the amplitude and phase angle of ACsignals, a temperature measurement circuit, self calibration anddiagnosis circuits and an embedded microprocessor for controlling signalgeneration and measuring module and sending and receiving signals to orfrom the computing unit 15.

The computing unit 15 preferably includes means for outputting controlvariables or commands to the measurement unit 14, means for receivingand recording measured temperature, real and imaginary impedance valuesfor a plurality of different frequencies, means for checking thevalidity of received data, means for scaling the received data into asuitable value range, means for calculating indicator quantities fromthe measured EIS, means for clustering the data into data patterns andmeans for determining at least one of characteristics of multiphasefluid mixtures from the indicator quantities.

By measuring the electrical impedance spectrum across the fluidmeasurement electrodes 11 a and 11 b, information about characteristicsof multiphase fluid mixtures can be identified. The multiphase fluidmixtures preferably include matter such as gas, solid, liquid ordifferent combinations of the above.

For example as shown in FIGS. 3A and 3B the effects of phase compositionon electrical impedance spectrum of slurries can be ascertained.Electrical impedance spectra for water only, water slurry containing 20%(by volume) sands, water slurry containing 12% magnetite and waterslurry containing 10% magnetite and 20% sands are represented by 31, 32,33, 34, respectively. FIG. 3A shows the spectra for the frequency rangeof 0.5 Hz to 1 MHz. In order to emphasise the effects of phasecomposition on EIS, the same spectra only in the frequency range of 100Hz to 1 MHz is shown in FIG. 3B. FIGS. 3A and 3B clearly indicate thatthe EIS is sensitive to the changes of phase composition of aqueousslurry mixtures. It is this sensitivity that provides the basis for thepresent invention. It can be also seen that the effect of the presenceof magnetite on the spectrum is substantially different from that ofsand. The presence of magnetite can cause a peak in the high frequencyrange of the spectrum, but sand cannot. The ability of the apparatus inthe present invention to distinguish the relative composition ofdifferent dispersed phases is based on their different effects on thespectrum. The measurement of the spectra as shown in FIGS. 3A and 3B canbe repeated at a few different amplitudes of the excitation signal andthe determination of the amplitude dependence of the spectra would allowthe further differentiation of factors causing the changes of the EIS.

In FIGS. 4A and 4B EIS is produced for sugar syrup having differentcrystal contents. In the case with white sugar (see FIG. 4A), the EISfor the unsaturated syrup 41 (containing 20% by volume water and 80%saturated syrup) is significantly different from that for saturatedsyrup 42. By adding 10% by weight white sugar crystals into thesaturated syrup, a spectrum 43 is produced. Electrical impedance spectrafor unsaturated raw sugar syrup, saturated raw sugar syrup and saturatedraw sugar syrup with 10% (by weight) raw sugar crystals are representedby 44, 45 and 46, respectively. By comparing the EIS for raw sugar inFIG. 4B with those for white sugar in FIG. 4A, it can be seen that thespectra for raw sugar syrup have a lower value of real impedance anddifferent spectrum patterns from those for white sugar. This is due tothe higher concentration of soluble impurity in raw sugar. As shown inFIGS. 4A and 4B, EIS can detect not only the crystal content of motherliquor but also the purity of mother liquor.

FIGS. 5A and 5B show examples of EIS curves for water-oil emulsions.Curve 51 shows the EIS for the water-in-oil emulsion with 25% (byvolume) water whereas curve 52 shows the EIS for the emulsion with 50%water. FIG. 5B shows EIS for oil-in-water emulsions, and the spectra for50% and 75% water are represented by 53 and 54, respectively. It can beseen that the EIS pattern for water-in-oil emulsions is different fromthat for oil-in-water emulsions. This difference will provide a basisfor identifying emulsion type using EIS.

FIG. 6 shows the EIS change of slurry with particle size under the samevolumetric concentration of solids. It can be seen that for thisparticular particles the EIS for 30 μm particle size 61 is significantlychanged to curve 62 when the particle size is reduced to 20 μm. Itshould be pointed out that EIS is not sensitive to particle size changewhen the size is higher than 50 μp. However, in situations where theparticle size is smaller than 50 μm, it is possible to monitor theparticle size by observing the change of EIS curve.

The froth phase in bubble flotation processes is a special type ofmultiphase fluid mixtures, in which the electrical and dielectricproperties of components are different from each other. For example, theconductivity of water is several orders of magnitude higher than thatfor mineral particles. From the viewpoint of electrical behaviour of thefroth phase, the inter-bubble lamellae containing water and solids canbe regarded as a complex network of electrical conductance, inductanceand capacitance. The structure of this network is sensitive to changesin the operating conditions of bubble flotation processes, and hence theeffects of operating conditions, such as reagent dosages, feed flowrateand froth depth, on the flotation performance are reflected on themeasured EIS. Therefore, the measurement of the electrical impedance ofthe froth phase over a wide range of excitation signal frequency wouldprobe into the performance of flotation processes.

FIG. 7 shows electrical impedance spectra measured in the froth phase ofbubble flotation processes of one fine coal under various operatingconditions. The spectra for 78%, 74% and 68% flotation yield arerepresented by 71, 72 and 73, respectively. It can be seen from thefigure that the EIS spectra is closely correlated with the productyield. For this particular coal, the spectrum 71 is favourable in termof product yield. This favourable spectrum pattern can be convenientlyused as the objective function for optimising operating conditions. Inthe bubble flotation of other materials, such as minerals, the patternof EIS of the froth phase may be different from that shown in FIG. 7.However, the favourable pattern of EIS and associated operatingconditions still can be identified using EIS as long as the flotationperformance is sensitive to the changes in operating conditions.

Examples presented in FIGS. 3 to 7 clearly demonstrate that theelectrical impedance spectrum can provide sufficient informationregarding to the characteristics of multiphase fluid mixtures. In orderto use these information for the on-line estimation of thecharacteristics of multiphase fluid mixtures, a mathematical or othertype of relationship between the EIS and its correspondingcharacteristics of multiphase fluid mixtures is required. Among thevarious approaches for describing and modeling phenomena that are toocomplex for analytical methods or empirical rules, artificialintelligent data analysis techniques, particularly the artificial neuralnetwork (ANN) have shown great potential as an effective method foridentifying or mapping complex non-linear relations without requiringspecific knowledge of the model structure. Artificial neural networktechniques are very efficient in computation due to the feedforwardnature and also have higher tolerance to errors in the input data setthan other parameter estimation approaches. Hence, a multiplayerperceptron artificial neural network (MLP-ANN) is a preferred but not anexclusive approach in the present invention to estimate characteristicsof interest from the measured EIS of multiphase fluid mixtures. Otherapproaches, such as multivariate regression and ANN based on fuzzy logicare also useful in correlating the measured EIS with the characteristicsof multiphase fluid mixtures.

Based on observations derived from EIS measurements taken using theaforementioned apparatus it is possible to employ an automated procedureto identify characteristics of multiphase fluid mixtures. FIG. 8 is aflow chart showing a method for implementing this automated procedure.

As illustrated in FIG. 8, when the power is ON, the measurement unit 14makes a diagnosis of itself and becomes initialised in step 80.Computing unit 15 then sends control variables to the measurement unit14 in step 81. Control variables include the amplitude of AC signalgenerated by 14, frequency range, number of measurement points in thefrequency range, and the like.

Once the measurement unit 14 receives control variables the electricalimpedance spectrum, temperature and optionally pH are measured andrecorded in step 82 using the aforementioned apparatus. It is preferredthat the measurement of EIS, temperature and pH in step 82 are repeatedseveral times in a short period of time and their average values areused for further processing. If the data is valid for a particularapplication as referenced by step 83 the computing unit is able toactivate a data processor so as to scale the data into a suitable rangeof values as referenced by step 84. Alternatively if the data is notvalid an alarm signal is provided to a display to notify an observerthat the invalid data occurs and the measurement and recording step 82is repeated.

After the data has been scaled into a suitable range the computing unit15 is programmed to calculate the indicator quantities from the scaledEIS data as referenced by step 85. Then a software program performs aclassification analysis of data pattern in step 86 to identify whetherthe EIS data pattern is unseen in the training stage of an artificialneural network (ANN) or in the development stage of a multivariateregression model. If the answer is yes an alarm signal is provided to adisplay to notify an observer that the new data pattern occurs and themeasurement and recording step 82 is repeated. If the new data patternrepeatedly occurs, the computing unit 15 is programmed to retrain an ANNmodel or refit a multivariate regression model using a data setincluding the new data pattern. Alternatively if the data pattern is nota new one an output of at least one of the characteristics of themultiphase fluid mixtures is produced by the computing unit in the step87. If there is no manual interruption then the measurement andrecording step of item 82 is repeated.

In the data validation step, as referenced by item 83, data with a lowprecision, values close to pre-specified limits and significant noiseare discarded to control the data quality for further processing. In ANNand multivariate analysis it is mandatory to scale the measured EIS andother data before the main business of analysis begins. This is becausethe measured EIS and other variables have different units and magnitudeof values. Scaling methods useful in the present invention includecolumn centring, standardisation and range scaling. Range scaling causethe values to fall between 0 to 1 or −1 to 1. These scaling methods areapplied only to columns (i.e. data points at a same frequency fromdifferent measurements).

In order to capture all important frequency and signal amplitudedependent information, a number of frequency points are usually used inthe measurement of EIS and the measurement is repeated with a fewdifferent amplitude of excitation signal. In applying mathematicalapproaches, such as artificial neural network and multivariateregression, to predict characteristics from measured EIS, the use of alldata points in a spectrum will result in a very large dimension ofinput. An unnecessary large dimension of input variables will haveadverse effects. For a fixed number of training data patterns, with theincrease of input variables it becomes more sparse in themulti-dimensional space, and therefore degrades the learningperformance. The generality of the trained ANN model may also be reduceddue to inclusion of irrelevant or unimportant input variables. Apartfrom irrelevant and unimportant variables that cause large dimension ofinput variables, there may be correlation's between EIS data pointsmeasured at frequencies close to each other. Correlated inputs make themodel more sensitive to the statistical peculiarities of the particulardata sample, and they accentuate the overfitting problem and limitgeneralisation. Therefore, it is an important step in the presentinvention to calculate or extract from EIS indicator quantities with amuch less number of variables but retaining sufficient information ofthe original spectrum.

The indicator quantities of the electrical impedance spectrum mayinclude the real and imaginary impedance values at a number of selectedfrequencies, first and second derivatives of the spectrum at a number ofselected frequencies, average values of imaginary impedance componentover a selective range of real impedance, the parameters of amathematical model for the representation of the impedance spectrum, andthe latent variables or principal components, summarising theinformation contained in the original impedance spectrum, calculatedfrom multivariate statistical methods, such as principal componentanalysis (PCA) and partial least-squares (PLS).

The method preferably includes calculating indicator quantities byfitting the electrical impedance spectrum to a mathematical model of theimpedance spectrum. The mathematical model may further includes anelectrical equivalent circuit model and empirical regression equations.

It is preferred that PCA implemented using an artificial neural network,as shown in FIG. 9, adapted with Hebbian learning or similar rules isused for calculating indicator quantities for the robustness. There arewell-known algorithms that analytically computer PCA, but they have tosolve matrix equations associated with singular value decomposition.When the matrices are ill-conditioned, the numerical solutions fail,while PCA neural networks provide more robust solutions.

If an indicator quantity data set has a pattern which has not been seenin the training stage of an ANN model or in the development stage of amultivariate regression model, the output of these model taking the dataset as input will be erroneous. Therefore, it is necessary to checkwhether the pattern of a new data set is new. The classification of datapatterns can be performed using ANN based approaches, such asunsupervised Bayesian clustering system, or the data reconstructionapproach associated with PCA. If the indicator quantities calculatedfrom a measured EIS (x) is represented by vector y, the reconstructedEIS is given byx′=W^(T)y,

where W is the weight matrix.

If the difference between the reconstructed EIS, x′ and the measuredEIS, x is larger than a specified threshold, the data pattern of themeasured EIS can be considered as a new one. If this new patternrepeatedly occurs, it will become necessary to retrain an ANN model orrefit a multivariate regression model.

In the prediction step, as referenced by item 87, the ANN model can bereplaced by a multivariate regression model, a pattern matchingalgorithm or even a lookup table. When a lookup table is used in step87, the comparison between indicator quantities of a measured EIS withreference indicator quantities will be used to determine characteristicsof multiphase fluid mixtures.

The output from an trained ANN model or other types of relationship,such as lookup tables can be numerical values or qualitative indices,such as classification index.

1. A method for determining at least one characteristic of a multiphasefluid including the steps of applying alternating energy of apredetermined amplitude to a portion of a multiphase fluid and measuringthe electrical impedance spectrum across the portion of multiphase fluidwhereby a characteristic of the multiphase fluid can be determined fromthe measured electrical impedance spectra.
 2. The method as claimed inclaim 1 including the step of applying alternating energy of a differentpredetermined amplitude to the portion of the multiphase fluid andmeasuring the electrical impedance spectrum across the portion of themultiphase fluid whereby the characteristic of the multiphase fluid canbe determined from the measured electrical impedance spectra.
 3. Themethod as claimed in claim 1 including the steps of applying a pluralityof alternating currents of different constant amplitude to the portionof the multiphase fluid and measuring the electrical impedance spectrumacross the portion of multiphase fluid whereby the characteristic of themultiphase fluid can be determined from the measured electricalimpedance spectra for each of the constant amplitudes.
 4. The method asclaimed in claim 1 including the steps of applying a plurality ofalternating voltages of different constant amplitude to the portion ofmultiphase fluid and measuring the electrical impedance spectrum acrossthe portion of multiphase fluid whereby the characteristic of themultiphase fluid can be determined from the measured electricalimpedance spectra for each amplitude of constant voltage.
 5. The methodas claimed in claim 1 including the step of providing at least twoelectrodes and applying alternating energy of the predeterminedamplitude to the portion of multiphase fluid between the electrodes. 6.The method as claimed in claim 1 including the step of providing threeor more electrodes to apply the alternating energy across.
 7. The methodas claimed in claim 1 including the step of transforming the measuredelectrical impedance spectrum into a plurality of indicator values usinga feature extraction algorithm.
 8. The method as claimed in claim 7including the step of determining from the indicator values at least onecharacteristic at least one phase constituent in the multiphase fluid.9. The method as claimed in claim 8 including the step of usingpredictive mathematical modelling to determine one or morecharacteristics of the multiphase constituents.
 10. The method asclaimed in claim 1 including the step of measuring the real andimaginary parts of the impedance over 0.1 Hz to 1 MHz.
 11. The method asclaimed in claim 10 including the step of measuring temperature and pHvalues of the multiphase fluid at each measured spectrum.
 12. The methodas claimed in claim 11 wherein the transforming step includes the stepof calculating a smoothed electrical impedance spectrum using asmoothing algorithm.
 13. The method as claimed in claim 12 wherein thestep of transforming includes calculating indicator values by fittingthe electrical impedance spectrum to a mathematical model of theimpedance spectrum.
 14. The method as claimed in claim 13 wherein themathematical model includes an electrical equivalent circuit model andemperical regression equations.
 15. The method as claimed in claim 14wherein the step of determining includes using a predictive mathematicalmodel.
 16. The method as claimed in claim 15 wherein the predictivemathematical model includes a trained artificial neural network and amulti variate regression model.
 17. The method as claimed in claim 16including the step of training and validating an artificial neuralnetwork with a number of indicator quantities with known characteristicsof multiphase fluid mixtures.
 18. The method as claimed in claim 17including the step of analysing the impedance spectrum using patternmatching algorithms to determine whether characteristics of a bubblefroth phase loaded with particles are favourable or not in terms of thegrade and yield of the flotation concentrate.
 19. The method as claimedin claim 17 including the step of analysing the impedance spectrum usingthe pattern matching algorithms to determine whether an oil/wateremulsion is of an oil continuous or water continuous type.
 20. Anapparatus for characterising multiphase fluid mixtures, the apparatuscomprising at least one pair of electrodes for meausuring at least onecharacteristic of a sample zone of a multiphase fluid locatedtherebetween, a field generation means for generating an electricalfield between the electrodes, a measuring means for measuring at leastone characteristic of the electrical field formed between the electrodesand a data processing means for collecting data from the measuringmeans, processing the data and outputting real and imaginary impedancedata for a constant amplitude of voltage or current generated by thefield generation means.
 21. The apparatus as claimed in claim 20including three electrodes configured to have a constant voltage betweenfirst and second electrodes and a constant voltage between second andthird electrodes.
 22. The apparatus as claimed in claim 20 includingfour electrodes configured to have a constant current applied by firstand second electrodes and by third and fourth electrodes.
 23. Anapparatus as claimed in claim 20 including a plurality of pairs ofelectrodes configured to have a constant current produced by someelectrodes and a constant voltage across other electrodes.
 24. Theapparatus as claimed in claim 20 wherein the field generation means isconfigured to generate frequencies between 0.1 Hz and 1 MHz.
 25. Theapparatus as claimed in claim 24 including a temperature sensor formeasuring the temperature of multiphase fluid within the sample zone anda pH value sensor for sensing the pH value of multiphase fluid withinthe sample zone.
 26. A method of analysing extraneous matter in a fluidincluding the steps of receiving impedance data, being data includingreal and imaginary impedance values measured across electrodes locatedin the fluid, recording the impedance spectrum at a plurality of timeintervals at a predetermined amplitude of energy applied across theelectrodes, calculating indicator quantities of the impedance spectrumfor the received impedance spectrum data, comparing indicator quantitiesof the impedance spectrum with reference indicator quantities anddetermining at least one characteristic of at least one phaseconstituent in the multiphase fluid for the comparing steps.
 27. Themethod as claimed in claim 26 wherein the indicator quantities includethe minimum number of quantities required to model the originalelectrical impedance spectrum.
 28. The method as claimed in claim 27wherein the indicator quantities include one or more of the real andimaginary impedance values at a number of selected frequencies, firstand second derivatives of the spectrum at a number of selectedfrequencies, average values of imaginary impedance component over aselective range of real impedance, the parameters of a mathematicalmodel for the r presentation of the impedance spectrum and the latentvariables or principle components summarising the information containedin the original impedance spectrum calculated from at least onemultivariate statistical method.
 29. The method as claimed in claim 28wherein the multivariate statistical method includes a principlecomponent analysis using an artificial neural network.
 30. The method asclaimed in claim 29 including a checking means to check whether apattern of a new data set of indicator quantity data fits apredetermined pattern.
 31. The method as claimed in claim 30 wherein thechecking means includes the step of representing indicator quantitiescalculated from a measured electrical impedance spectrum EIS(x) byvector y and calculating the reconstructed EIS given by x′=W^(T)y whereW is the weight matrix, whereby if for the difference between thereconstructed EIS, x′ and the measured EIS, x is larger than a specifiedthreshold, the data pattern of the measured EIS is recorded as a newone.